The purpose of this research shed light on the analysis of the relationship between the knowledge gap and the strategic performance gap and diagnose the level of impact this relationship in building a learning organization, and sought search to achieve a number of goals, cognitive and Applied been tested nature of the relationship and effect between variables in a sample size (62) of the managers of banks civil in Baghdad (Baghdad, Gulf, Assyria, Union, Elaf) and focused research problem in question is bold is whether the analysis of the relationship between the knowledge gap and the performance gap strategic leads to recognize organizations need to shift to organizations educated, either in the side of the field was the problem about the extent of realize the organizations surveyed for this equation in its ability to create a kind of mismatch between the analysis of the knowledge gap analysis and gap strategic performance, and analysis of research data used statistical program ready (SPSS) program (Excel) and the most important Statistical tools used in the analysis is the "arithmetic mean, standard deviation and coefficient of difference, and correlation coefficient of Spearman, and path analysis "and was the most prominent results that have been reached and there is a correlation and impact between the analysis of the knowledge gap and gap analysis of performance and strategic between building learning organization, as well as increase the impact of the knowledge gap about the possibility of switching to the educated through the gap strategic performance, and research has included four axes went first to the methodology and second to the theoretical framing and the third to view and analyze the results and test hypotheses in the fourth devoted to the conclusions and recommendations.
Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system
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